第 0 天支援：Claude Opus 4.6

2026年2月5日

Sameer Kankute

SWE @ LiteLLM (LLM Translation)

Ishaan Jaffer

CTO, LiteLLM

Krrish Dholakia

CEO, LiteLLM

LiteLLM 現在於第 0 天支援 Claude Opus 4.6。您可透過 LiteLLM AI Gateway 在 Anthropic、Azure、Vertex AI 和 Bedrock 上使用它。

Docker 映像

docker pull ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.80.0-stable.opus-4-6

用法 - Anthropic

LiteLLM Proxy

1. 設定 config.yaml

model_list:
  - model_name: claude-opus-4-6
    litellm_params:
      model: anthropic/claude-opus-4-6
      api_key: os.environ/ANTHROPIC_API_KEY

2. 啟動 proxy

docker run -d \
  -p 4000:4000 \
  -e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY \
  -v $(pwd)/config.yaml:/app/config.yaml \
  ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.80.0-stable.opus-4-6 \
  --config /app/config.yaml

3. 測試它！

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
  "model": "claude-opus-4-6",
  "messages": [
    {
      "role": "user",
      "content": "what llm are you"
    }
  ]
}'

用法 - Azure

LiteLLM Proxy

1. 設定 config.yaml

model_list:
  - model_name: claude-opus-4-6
    litellm_params:
      model: azure_ai/claude-opus-4-6
      api_key: os.environ/AZURE_AI_API_KEY
      api_base: os.environ/AZURE_AI_API_BASE  # https://<resource>.services.ai.azure.com

2. 啟動 proxy

docker run -d \
  -p 4000:4000 \
  -e AZURE_AI_API_KEY=$AZURE_AI_API_KEY \
  -e AZURE_AI_API_BASE=$AZURE_AI_API_BASE \
  -v $(pwd)/config.yaml:/app/config.yaml \
  ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.80.0-stable.opus-4-6 \
  --config /app/config.yaml

3. 測試它！

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
  "model": "claude-opus-4-6",
  "messages": [
    {
      "role": "user",
      "content": "what llm are you"
    }
  ]
}'

用法 - Vertex AI

LiteLLM Proxy

1. 設定 config.yaml

model_list:
  - model_name: claude-opus-4-6
    litellm_params:
      model: vertex_ai/claude-opus-4-6
      vertex_project: os.environ/VERTEX_PROJECT
      vertex_location: us-east5

2. 啟動 proxy

docker run -d \
  -p 4000:4000 \
  -e VERTEX_PROJECT=$VERTEX_PROJECT \
  -e GOOGLE_APPLICATION_CREDENTIALS=/app/credentials.json \
  -v $(pwd)/config.yaml:/app/config.yaml \
  -v $(pwd)/credentials.json:/app/credentials.json \
  ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.80.0-stable.opus-4-6 \
  --config /app/config.yaml

3. 測試它！

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
  "model": "claude-opus-4-6",
  "messages": [
    {
      "role": "user",
      "content": "what llm are you"
    }
  ]
}'

用法 - Bedrock

LiteLLM Proxy

1. 設定 config.yaml

model_list:
  - model_name: claude-opus-4-6
    litellm_params:
      model: bedrock/anthropic.claude-opus-4-6-v1
      aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
      aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
      aws_region_name: us-east-1

2. 啟動 proxy

docker run -d \
  -p 4000:4000 \
  -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
  -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
  -v $(pwd)/config.yaml:/app/config.yaml \
  ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.80.0-stable.opus-4-6 \
  --config /app/config.yaml

3. 測試它！

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
  "model": "claude-opus-4-6",
  "messages": [
    {
      "role": "user",
      "content": "what llm are you"
    }
  ]
}'

進階功能

壓縮

/chat/completions
/v1/messages

Litellm 支援為新的 claude-opus-4-6 啟用壓縮。

啟用壓縮

若要啟用壓縮，請使用 context_management 參數與 compact_20260112 編輯類型：

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
  "model": "claude-opus-4-6",
  "messages": [
    {
      "role": "user",
      "content": "What is the weather in San Francisco?"
    }
  ],
  "context_management": {
    "edits": [
      {
        "type": "compact_20260112"
      }
    ]
  },
  "max_tokens": 100
}'

Anthropic 支援的所有 context_management 參數都受到支援，且可直接加入。Litellm 會自動在請求中加入 compact-2026-01-12 beta 標頭。

啟用壓縮可在保留關鍵資訊的同時縮減 context 大小。啟用壓縮時，LiteLLM 會自動加入 compact-2026-01-12 beta 標頭。

資訊

提供者支援： 壓縮支援 Anthropic、Azure AI 和 Vertex AI。不支援 Bedrock（Invoke 或 Converse APIs）。

curl --location 'http://0.0.0.0:4000/v1/messages' \
--header 'x-api-key: sk-12345' \
--header 'content-type: application/json' \
--data '{
    "model": "claude-opus-4-6",
    "max_tokens": 4096,
    "messages": [
        {
            "role": "user",
            "content": "Hi"
        }
    ],
    "context_management": {
        "edits": [
            {
                "type": "compact_20260112"
            }
        ]
    }
}'

含壓縮區塊的回應

回應將在 provider_specific_fields.compaction_blocks 中包含壓縮摘要：

{
  "id": "chatcmpl-a6c105a3-4b25-419e-9551-c800633b6cb2",
  "created": 1770357619,
  "model": "claude-opus-4-6",
  "object": "chat.completion",
  "choices": [
    {
      "finish_reason": "length",
      "index": 0,
      "message": {
        "content": "I don't have access to real-time data, so I can't provide the current weather in San Francisco. To get up-to-date weather information, I'd recommend checking:\n\n- **Weather websites** like weather.com, accuweather.com, or wunderground.com\n- **Search engines** – just Google \"San Francisco weather\"\n- **Weather apps** on your phone (e.g., Apple Weather, Google Weather)\n- **National",
        "role": "assistant",
        "provider_specific_fields": {
          "compaction_blocks": [
            {
              "type": "compaction",
              "content": "Summary of the conversation: The user requested help building a web scraper..."
            }
          ]
        }
      }
    }
  ],
  "usage": {
    "completion_tokens": 100,
    "prompt_tokens": 86,
    "total_tokens": 186
  }
}

在後續請求中使用壓縮區塊

若要使用壓縮繼續對話，請在 assistant 訊息的 provider_specific_fields 中包含壓縮區塊：

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
  "model": "claude-opus-4-6",
  "messages": [
    {
      "role": "user",
      "content": "How can I build a web scraper?"
    },
    {
      "role": "assistant",
      "content": [
        {
          "type": "text",
          "text": "Certainly! To build a basic web scraper, you'll typically use a programming language like Python along with libraries such as `requests` (for fetching web pages) and `BeautifulSoup` (for parsing HTML). Here's a basic example:\n\n```python\nimport requests\nfrom bs4 import BeautifulSoup\n\nurl = 'https://example.com'\nresponse = requests.get(url)\nsoup = BeautifulSoup(response.text, 'html.parser')\n\n# Extract and print all text\ntext = soup.get_text()\nprint(text)\n```\n\nLet me know what you're interested in scraping or if you need help with a specific website!"
        }
      ],
      "provider_specific_fields": {
        "compaction_blocks": [
          {
            "type": "compaction",
            "content": "Summary of the conversation: The user asked how to build a web scraper, and the assistant gave an overview using Python with requests and BeautifulSoup."
          }
        ]
      }
    },
    {
      "role": "user",
      "content": "How do I use it to scrape product prices?"
    }
  ],
  "context_management": {
    "edits": [
      {
        "type": "compact_20260112"
      }
    ]
  },
  "max_tokens": 100
}'

串流支援

串流模式也支援壓縮區塊。您將收到：

壓縮區塊開始時的 compaction_start 事件
含有壓縮內容的 compaction_delta 事件
累積的 compaction_blocks，位於 provider_specific_fields 中

自適應思考

備註

使用 reasoning_effort 搭配 Claude Opus 4.6 時，所有值（low、medium、high、max）都會對應到 thinking: {type: "adaptive"}。透過 thinking: {type: "enabled", budget_tokens: ...} 設定的明確預算在 Opus 4.6 上仍可運作，但已棄用且不再建議；請改用帶有 output_config.effort 的自適應思考（請參閱下方的 Effort Levels）來控制思考深度。

/chat/completions
/v1/messages
Native thinking param

LiteLLM 透過 reasoning_effort 參數支援自適應思考：

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
  "model": "claude-opus-4-6",
  "messages": [
    {
      "role": "user",
      "content": "Solve this complex problem: What is the optimal strategy for..."
    }
  ],
  "reasoning_effort": "high"
}'

使用 thinking 參數搭配 type: "adaptive" 以啟用自適應思考模式：

curl --location 'http://0.0.0.0:4000/v1/messages' \
--header 'x-api-key: sk-12345' \
--header 'content-type: application/json' \
--data '{
    "model": "claude-opus-4-6",
    "max_tokens": 16000,
    "thinking": {
        "type": "adaptive"
    },
    "messages": [
        {
            "role": "user",
            "content": "Explain why the sum of two even numbers is always even."
        }
    ]
}'

直接使用 thinking 參數，透過 SDK 啟用自適應思考：

import litellm

response = litellm.completion(
  model="anthropic/claude-opus-4-6",
  messages=[{"role": "user", "content": "Solve this complex problem: What is the optimal strategy for..."}],
  thinking={"type": "adaptive"},
)

努力等級

/chat/completions
/v1/messages

可用的四個 effort levels：low、medium、high（預設）以及 max。請直接透過 output_config 參數傳入：

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
  "model": "claude-opus-4-6",
  "messages": [
    {
      "role": "user",
      "content": "Explain quantum computing"
    }
  ],
  "output_config": {
        "effort": "medium"
    }
}'

您可以使用 reasoning effort 搭配 output_config，以便更精細地控制模型。

可用的四個 effort levels：low、medium、high（預設）以及 max。請直接透過 output_config 參數傳入：

curl --location 'http://0.0.0.0:4000/v1/messages' \
--header 'x-api-key: sk-12345' \
--header 'content-type: application/json' \
--data '{
    "model": "claude-opus-4-6",
    "max_tokens": 4096,
    "messages": [
        {
            "role": "user",
            "content": "Explain quantum computing"
        }
    ],
    "output_config": {
        "effort": "medium"
    }
}'

1M Token 上下文（Beta）

Opus 4.6 支援 1M token context。超過 200k tokens 的提示詞將適用進階定價（每百萬 input/output tokens 為 $10/$37.50）。LiteLLM 支援 1M token contexts 的成本計算。

/chat/completions
/v1/messages

若要使用 1M token context 視窗，您需要將來自用戶端的 anthropic-beta 標頭轉送到 LLM 提供者。

步驟 1：在您的設定中啟用標頭轉送

general_settings:
  forward_client_headers_to_llm_api: true

步驟 2：使用 beta 標頭傳送請求

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--header 'anthropic-beta: context-1m-2025-08-07' \
--data '{
  "model": "claude-opus-4-6",
  "messages": [
    {
      "role": "user",
      "content": "Analyze this large document..."
    }
  ]
}'

若要使用 1M token context 視窗，您需要將來自用戶端的 anthropic-beta 標頭轉送到 LLM 提供者。

步驟 1：在您的設定中啟用標頭轉送

general_settings:
  forward_client_headers_to_llm_api: true

步驟 2：使用 beta 標頭傳送請求

curl --location 'http://0.0.0.0:4000/v1/messages' \
--header 'x-api-key: sk-12345' \
--header 'anthropic-beta: context-1m-2025-08-07' \
--header 'content-type: application/json' \
--data '{
    "model": "claude-opus-4-6",
    "max_tokens": 16000,
    "messages": [
        {
            "role": "user",
            "content": "Analyze this large document..."
        }
    ]
}'

提示

您可以透過逗號分隔來合併多個 beta 標頭：

--header 'anthropic-beta: context-1m-2025-08-07,compact-2026-01-12'

僅限美國推論

可用價格為 token 價格的 1.1×。LiteLLM 會自動追蹤僅限美國推論的成本。

/chat/completions
/v1/messages

使用 inference_geo 參數來指定僅限美國推論：

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
  "model": "claude-opus-4-6",
  "messages": [
    {
      "role": "user",
      "content": "What is the capital of France?"
    }
  ],
  "inference_geo": "us"
}'

LiteLLM 會在成本追蹤中自動套用僅限美國推論的 1.1× 價格乘數。

使用 inference_geo 參數來指定僅限美國推論：

curl --location 'http://0.0.0.0:4000/v1/messages' \
--header 'x-api-key: sk-12345' \
--header 'content-type: application/json' \
--data '{
    "model": "claude-opus-4-6",
    "max_tokens": 4096,
    "messages": [
        {
            "role": "user",
            "content": "What is the capital of France?"
        }
    ],
    "inference_geo": "us"
}'

LiteLLM 會在成本追蹤中自動套用僅限美國推論的 1.1× 價格乘數。

快速模式

資訊

快速模式僅支援 Anthropic 提供者（anthropic/claude-opus-4-6）。Azure AI、Vertex AI 或 Bedrock 上均不可用。

定價：

標準：每 MTok $5 input / $25 output
快速：每 MTok $30 input / $150 output（6× premium）

/chat/completions
/v1/messages

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
  "model": "claude-opus-4-6",
  "messages": [
    {
      "role": "user",
      "content": "Refactor this module..."
    }
  ],
  "max_tokens": 4096,
  "speed": "fast"
}'

使用 OpenAI SDK：

import openai

client = openai.OpenAI(
    api_key="your-litellm-key",
    base_url="http://0.0.0.0:4000"
)

response = client.chat.completions.create(
    model="claude-opus-4-6",
    messages=[{"role": "user", "content": "Refactor this module..."}],
    max_tokens=4096,
    extra_body={"speed": "fast"}
)

使用 LiteLLM SDK：

from litellm import completion

response = completion(
    model="anthropic/claude-opus-4-6",
    messages=[{"role": "user", "content": "Refactor this module..."}],
    max_tokens=4096,
    speed="fast"
)

LiteLLM 會在使用量與成本計算中自動追蹤快速模式較高的成本。

curl --location 'http://0.0.0.0:4000/v1/messages' \
--header 'x-api-key: sk-12345' \
--header 'content-type: application/json' \
--data '{
    "model": "claude-opus-4-6",
    "max_tokens": 4096,
    "speed": "fast",
    "messages": [
        {
            "role": "user",
            "content": "Refactor this module..."
        }
    ]
}'

LiteLLM 會自動：

加入 fast-mode-2026-02-01 beta 標頭
在成本計算中追蹤 6× premium 定價

Docker 映像​

用法 - Anthropic​

用法 - Azure​

用法 - Vertex AI​

用法 - Bedrock​

進階功能​

壓縮​

自適應思考​

努力等級​

1M Token 上下文（Beta）​

僅限美國推論​

快速模式​

Docker 映像

用法 - Anthropic

用法 - Azure

用法 - Vertex AI

用法 - Bedrock

進階功能

壓縮

自適應思考

努力等級

1M Token 上下文（Beta）

僅限美國推論

快速模式